Citi, HSBC, StanChart Adopt Ant International’s Forex AI Tool, As Global Banks Turn to...

Ant International has launched an upgraded artificial intelligence model designed for financial forecasting, signing partnerships with six major global banks as lenders accelerate efforts to deploy specialized AI systems to manage liquidity, foreign exchange and other balance-sheet risks.

The Singapore-based fintech company on Thursday unveiled the Falcon Time-Series Transformer Model 2.0, an upgraded forecasting system aimed specifically at financial applications. Kelvin Li, Ant International's general manager of platform technology, said the model has been adopted through partnerships with six major banks, including Citi, HSBC, Deutsche Bank, Standard Chartered and Barclays.

The partnerships underline the growing push by financial institutions to move AI beyond customer-service applications and into core banking functions where more accurate forecasts can directly affect trading, treasury management and capital allocation.

Financial institutions manage large and constantly changing pools of cash across currencies, markets and jurisdictions. Errors in forecasting can leave banks holding excess liquidity that earns little return or force them to obtain funding at higher costs. More accurate predictions of cash flows, foreign exchange movements and liquidity requirements can therefore produce substantial savings.

Li said Falcon 2.0 is designed specifically for such financial scenarios and has advantages over general-purpose AI models.

General-purpose large models have "yet to achieve a universal breakthrough in the financial sector," Li said, arguing that specialized systems can be better suited to highly structured financial data and forecasting requirements.

Ant International said the model's forecasting capabilities can reduce foreign-exchange hedging and allocation costs by more than 60%. Such savings could be significant for banks and multinational companies with large cross-border exposures, although the actual benefit will depend on the quality of underlying data, the markets covered and how institutions integrate the technology into their existing risk-management systems.

The launch comes as banks globally increase spending on AI amid pressure to improve productivity and automate complex processes. Financial institutions have been among the largest corporate adopters of AI, using the technology for fraud detection, risk assessment, trading, compliance, customer service and software development.

The next phase is focused on specialized systems capable of operating within tightly controlled financial environments. Unlike consumer-facing generative AI, treasury and risk-management applications require reliable numerical forecasting, explainability, data security, and strict controls over how models influence financial decisions.

Time-series models are relevant to these applications because they are designed to identify patterns and relationships in sequential data. In banking, that can include historical cash flows, currency movements, interest rates, transaction volumes, and other market indicators.

Ant International's strategy puts it in competition with both established financial-technology providers and technology companies seeking to supply AI infrastructure to banks. The company's focus on specialized financial models could also allow it to target a market where institutions are reluctant to rely entirely on general-purpose AI because of the consequences of inaccurate outputs.

The move is part of Ant International's broader international expansion. The company, the overseas affiliate of Chinese fintech group Ant Group, raised $1.2 billion in its latest equity fundraising last month as it seeks to expand its business.

The capital raising gives the company additional resources as competition intensifies for enterprise AI contracts and financial institutions become more selective about the systems they adopt.

For global banks, the appeal of specialized AI is increasingly tied to measurable financial outcomes rather than the technology's novelty. Forecasting improvements that lower hedging costs, optimize liquidity positions, or improve capital allocation can provide a direct return on AI investment.

The challenge is that financial markets are highly dynamic. Models trained on historical patterns can struggle when market conditions change abruptly, meaning banks are likely to require continuous monitoring, human oversight, and safeguards around AI-generated forecasts.

Ant International's latest model therefore marks a broader shift in financial AI: from general-purpose experimentation toward specialized systems designed to solve specific, high-value problems inside banks.

As banks deepen their use of AI in treasury and risk management, the ability to demonstrate measurable improvements in forecasting accuracy and operating costs is expected to become a key factor in determining which AI platforms gain widespread adoption across the financial sector.